Learning Barycentric Representations of 3D Shapes for Sketch-based 3D Shape Retrieval

Retrieving 3D shapes with sketches is a challenging problem since 2D sketches and 3D shapes are from two heterogeneous domains, which results in large discrepancy between them. In this paper, we propose to learn barycenters of 2D projections of 3D shapes for sketch-based 3D shape retrieval. Specifically, we first use two deep convolutional neural networks (CNNs) to extract deep features of sketches and 2D projections of 3D shapes. For 3D shapes, we then compute the Wasserstein barycenters of deep features of multiple projections to form a barycentric representation. Finally, by constructing a metric network, a discriminative loss is formulated on the Wasserstein barycenters of 3D shapes and sketches in the deep feature space to learn discriminative and compact 3D shape and sketch features for retrieval. The proposed method is evaluated on the SHREC'13 and SHREC'14 sketch track benchmark datasets. Compared to the state-of-the-art methods, our proposed method can significantly improve the retrieval performance.